# Accelerating Split Federated Learning over Wireless Communication Networks

arXiv: https://arxiv.org/abs/2310.15584 · HTML: https://mlsanity.com/paper/2310.15584/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 38 references that are in the map.
- First posted on arXiv 24 Oct 2023.

## Builds on (most cited first, 22 of 22)

- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Federated Learning: Challenges, Methods, and Future Directions](https://mlsanity.com/paper/1908.07873/) (2019)
- [Robust and Communication-Efficient Federated Learning from Non-IID Data](https://mlsanity.com/paper/1903.02891/) (2019)
- [Split learning for health: Distributed deep learning without sharing raw patient data](https://mlsanity.com/paper/1812.00564/) (2018)
- [SplitFed: When Federated Learning Meets Split Learning](https://mlsanity.com/paper/2004.12088/) (2020)
- [Distributed learning of deep neural network over multiple agents](https://mlsanity.com/paper/1810.06060/) (2018)
- [Deep Face Recognition: A Survey](https://mlsanity.com/paper/1804.06655/) (2018)
- [Edge Intelligence: Paving the Last Mile of Artificial Intelligence with Edge Computing](https://mlsanity.com/paper/1905.10083/) (2019)
- [Model Pruning Enables Efficient Federated Learning on Edge Devices](https://mlsanity.com/paper/1909.12326/) (2019)
- [Broadband Analog Aggregation for Low-Latency Federated Edge Learning (Extended Version)](https://mlsanity.com/paper/1812.11494/) (2018)
- [Energy Efficient Federated Learning Over Wireless Communication Networks](https://mlsanity.com/paper/1911.02417/) (2019)
- [Edge AI: On-Demand Accelerating Deep Neural Network Inference via Edge Computing](https://mlsanity.com/paper/1910.05316/) (2019)
- [One-Bit Over-the-Air Aggregation for Communication-Efficient Federated Edge Learning: Design and Convergence Analysis](https://mlsanity.com/paper/2001.05713/) (2020)
- [Joint Device Scheduling and Resource Allocation for Latency Constrained Wireless Federated Learning](https://mlsanity.com/paper/2007.07174/) (2020)
- [Speeding Up Distributed Machine Learning Using Codes](https://mlsanity.com/paper/1512.02673/) (2015)
- [Split Learning over Wireless Networks: Parallel Design and Resource Management](https://mlsanity.com/paper/2204.08119/) (2022)
- [Scheduling for Cellular Federated Edge Learning with Importance and Channel Awareness](https://mlsanity.com/paper/2004.00490/) (2020)
- [Energy-Efficient Federated Edge Learning with Joint Communication and Computation Design](https://mlsanity.com/paper/2003.00199/) (2020)
- [Optimal Model Placement and Online Model Splitting for Device-Edge Co-Inference](https://mlsanity.com/paper/2105.13618/) (2021)
- [Accelerating Federated Edge Learning via Topology Optimization](https://mlsanity.com/paper/2204.00489/) (2022)
- [Client Selection for Federated Bayesian Learning](https://mlsanity.com/paper/2212.05492/) (2022)

## Built on by (most cited first, 16 of 16)

- [Federated Split Learning with Model Pruning and Gradient Quantization in Wireless Networks](https://mlsanity.com/paper/2412.06414/) (2024)
- [Adaptive Split Learning over Energy-Constrained Wireless Edge Networks](https://mlsanity.com/paper/2403.05158/) (2024)
- [SCALA: Split Federated Learning with Concatenated Activations and Logit Adjustments](https://mlsanity.com/paper/2405.04875/) (2024)
- [Optimizing Model Splitting and Device Task Assignment for Deceptive Signal Assisted Private Multi-hop Split Learning](https://mlsanity.com/paper/2507.07323/) (2025)
- [Optimizing Split Points for Error-Resilient SplitFed Learning](https://mlsanity.com/paper/2405.19453/) (2024)
- [Semantic Meta-Split Learning: A TinyML Scheme for Few-Shot Wireless Image Classification](https://mlsanity.com/paper/2409.12978/) (2024)
- [Efficient Split Federated Learning for Large Language Models over Communication Networks](https://mlsanity.com/paper/2504.14667/) (2025)
- [Fast AI Model Partition for Split Learning over Edge Networks](https://mlsanity.com/paper/2507.01041/) (2025)
- [ASFL: An Adaptive Model Splitting and Resource Allocation Framework for Split Federated Learning](https://mlsanity.com/paper/2603.04437/) (2026)
- [Personalized Hierarchical Split Federated Learning in Wireless Networks](https://mlsanity.com/paper/2411.06042/) (2024)
- [A Two-Timescale Approach for Wireless Federated Learning with Parameter Freezing and Power Control](https://mlsanity.com/paper/2504.01752/) (2025)
- [Communication and Computation Efficient Split Federated Learning in O-RAN](https://mlsanity.com/paper/2508.02534/) (2025)
- [Federated Split Learning for Resource-Constrained Robots in Industrial IoT: Framework Comparison, Optimization Strategies, and Future Directions](https://mlsanity.com/paper/2510.05713/) (2025)
- [Device Association and Resource Allocation for Hierarchical Split Federated Learning in Space-Air-Ground Integrated Network](https://mlsanity.com/paper/2601.13817/) (2026)
- [GQ-FSL: Green Quantized Federated Split Learning Framework for Wireless Edge Networks](https://mlsanity.com/paper/2607.29659/) (2026)
- [Polar Code Based Federated Learning: Convergence Analysis and Resource Allocation](https://mlsanity.com/paper/2608.13961/) (2026)

## Abstract (arXiv)

The development of artificial intelligence (AI) provides opportunities for the promotion of deep neural network (DNN)-based applications. However, the large amount of parameters and computational complexity of DNN makes it difficult to deploy it on edge devices which are resource-constrained. An efficient method to address this challenge is model partition/splitting, in which DNN is divided into two parts which are deployed on device and server respectively for co-training or co-inference. In this paper, we consider a split federated learning (SFL) framework that combines the parallel model training mechanism of federated learning (FL) and the model splitting structure of split learning (SL). We consider a practical scenario of heterogeneous devices with individual split points of DNN. We formulate a joint problem of split point selection and bandwidth allocation to minimize the system latency. By using alternating optimization, we decompose the problem into two sub-problems and solve them optimally. Experiment results demonstrate the superiority of our work in latency reduction and accuracy improvement.